Technical requirements

In this chapter, we will use the Python libraries Matplotlib, pandas, NumPy, scikit-learn, and Feature-engine. If you need to install Python, the free Anaconda Python distribution (https://www.anaconda.com/) includes most numerical computing libraries.

feature-engine can be installed with pip as follows:

pip install feature-engine

If you use Anaconda, you can install feature-engine with conda:

conda install -c conda-forge feature_engine

Note

The recipes from this chapter were created using the latest versions of the Python libraries at the time of publishing. You can check the versions in the requirements.txt file in the accompanying GitHub repository, at https://github.com/PacktPublishing/Python-Feature-engineering-Cookbook-Third-Edition/blob/main/requirements.txt.

We will use the Credit Approval dataset from the UCI Machine Learning Repository (https://archive.ics.uci.edu/), licensed under the CC BY 4.0 creative commons attribution: https://creativecommons.org/licenses/by/4.0/legalcode. You’ll find the dataset at this link: http://archive.ics.uci.edu/dataset/27/credit+approval.

I downloaded and modified the data as shown in this notebook: https://github.com/PacktPublishing/Python-Feature-engineering-Cookbook-Third-Edition/blob/main/ch01-missing-data-imputation/credit-approval-dataset.ipynb

We will also use the air passenger dataset located in Facebook’s Prophet GitHub repository (https://github.com/facebook/prophet/blob/main/examples/example_air_passengers.csv), licensed under the MIT license: https://github.com/facebook/prophet/blob/main/LICENSE

I modified the data as shown in this notebook: https://github.com/PacktPublishing/Python-Feature-engineering-Cookbook-Third-Edition/blob/main/ch01-missing-data-imputation/air-passengers-dataset.ipynb

You’ll find a copy of the modified data sets in the accompanying GitHub repository: https://github.com/PacktPublishing/Python-Feature-engineering-Cookbook-Third-Edition/blob/main/ch01-missing-data-imputation/